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Artificial intelligence (AI) has become increasingly popular in various aspects of business. According to a survey by Forbes Advisor, the top applications of AI in business include customer service, cybersecurity and fraud management, customer relationship management, digital personal assistants, inventory management, and content production. Additionally, AI is used for product recommendations, accounting, supply chain operations, recruitment and talent sourcing, and audience segmentation.

One variant of AI, generative AI, has gained significant attention and is predicted to reshape the competitive landscape in various industries. CEOs recognize the competitive advantage that organizations with advanced generative AI have. However, the adoption of AI is no longer limited to big businesses, as more accessible and dedicated solutions are developed to enhance different aspects of business.

Successfully harnessing the potential of AI requires a methodical approach. Its not enough to simply download a version of generative AI; organizations need to properly integrate AI models into operational systems. Selecting, governing, analyzing, and applying data across the enterprise are crucial for the effective use of AI. Companies should analyze their processes, customer interactions, and data assets to identify areas where AI can make the most impact.

There may be skepticism about the impact of AI, particularly in the finance function. Some concerns include unclear business objectives, poor data quality, lack of talent, and a lack of collaboration between teams. To introduce AI into the workflow effectively, organizations should assess their AI readiness, visualize specific use cases and desired outcomes, and address barriers to adoption.

When applying AI, companies should outline clear goals and prioritize experimental rollouts. Additionally, a comprehensive proof of technology is essential to determine compatibility and integration with existing environments. To build trust in AI, organizations should infuse AI into their culture and incentivize teams to create business value with AI. Data security and compliance should also be considered when deploying AI tools.

Scaling AI across the organization can be challenging, as observed in the experience with robotic process automation (RPA). Many companies struggle to deploy responsible AI algorithms and models at scale. While AI has reached its inflection point, only a small percentage of organizations have successfully scaled their AI initiatives across the enterprise.

Overall, the popular applications of AI in business are diverse, and organizations need to approach AI adoption strategically to fully leverage its potential.

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Most Popular Applications of Artificial Intelligence (AI) in Business - Fagen wasanni